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    題名: Opinion Mining for Multiple Types of Emotion-Embedded Products/Services through Evolution Strategy
    作者: Yang, Heng-Li;Lin, Qing-Feng
    楊亨利
    Yang, Heng-Li
    貢獻者: 資管系
    關鍵詞: Chinese corpus;Evolutionary strategy;Multiple polarities;Opinion mining;Optimization;Sentiment analysis
    日期: 2018-06
    上傳時間: 2018-10-05 16:31:04 (UTC+8)
    摘要: Since the advent of blogging, microblogging, and social networking sites, researchers and practitioners have been increasingly concerned with the problem of obtaining useful evaluations from web-based opinion articles in a process known as opinion mining or sentiment analysis. In this study, we focused on reviews based on highly emotion-embedded products/services, such as movies, music, and drama. Furthermore, we tried to solve the multiple polarities problem for the same review word for multiple types of product/service. First, we collected text written in Chinese from a Taiwanese movie forum. In our proposed approach, we applied an evolutionary strategy algorithm to optimize the weight tables corresponding to two different types of movies: horror and drama movies. The experimental results indicated that the proposed method performed better than conventional methods when considering only one generalized type. Further, we employed a new multi-class support vector machine approach for predicting opinions at the document level. We used seven measures to describe the characteristics of an overall document, including the central tendency, dispersion, and shape of the predicted sentence value distribution, where the fluctuations in these values corresponded to their positions in the document. We also demonstrated the effectiveness of this approach for identifying opinions at the document level.
    關聯: Expert Systems with Applications, Vol.99, pp.44-55
    資料類型: article
    DOI 連結: https://doi.org/10.1016/j.eswa.2018.01.022
    DOI: 10.1016/j.eswa.2018.01.022
    顯示於類別:[資訊管理學系] 期刊論文

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